Machine learning can play an essential role in building a decision-making support system for use in clinical settings that helps quicken diagnosis and that can predict the risk of disease, in this case for heart failure. Unfortunately, a lack of trust from clinicians arises when the decision-making process of the models is not well explained, especially when the model applies to different demographics but is able to make a similar disease diagnosis. This study uses the SHAP method to explore the use of explainable artificial intelligence for model building through transfer learning with two different resources. We built a LightGBM predictive model that employs a novel cross-domain approach to improve a poor, low resource dataset by leveraging a rich, high resource dataset. This study revealed that this crossdomain approach enhanced the predictive performance of the model, with an absolute improvement of 12.86% in F1-score. Furthermore, the proposed approach still allowed the model to be interpreted with the XAI approach: 80% of the top 20 features of both cohorts that appeared in the feature importance analysis were overlap.

Tachycardia, characterized by an abnormally high heart rate, is a significant indicator of cardiac dysfunction requiring timely detection. This study proposes a lightweight, computationally efficient, data-driven machine learning pipeline for real-time binary tachycardia detection (Normal vs. Tachycardia) using electrocardiogram (ECG) signals. The system integrates Butterworth filtering, Pan-Tompkins R-peak detection, and feature extraction using heart rate variability (HRV) and discrete wavelet transform (DWT) coefficients. Five classical machine learning models—Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and a simple Neural Network—were evaluated on PhysioNet datasets. Random Forest achieved the highest performance with $\mathbf{9 8 . 0 0 \%}$ accuracy, $99.00 \%$ precision, and $\mathbf{9 8 . 5 8 \%}$ sensitivity. The results demonstrate that an optimized classical ML pipeline can provide reliable, real-time tachycardia detection with low computational overhead, making it highly suitable for integration into wearable health-monitoring systems where power and processing resources are constrained.

Software as a Medical Device (SaMD) has become indispensable in diagnostics, treatment planning, and patient monitoring. While high-income countries have introduced clear regulatory frameworks, Bangladesh and many low- and middle-income countries (LMICs) still lack tailored pathways for medical software approval (IMDRF. Software as a Medical Device (SaMD): Key Definitions (IMDRF/SaMD WG/N10FINAL:(2013)); IMDRF. Software as a Medical Device (SaMD): Clinical Evaluation (IMDRF/SaMD WG/N41FINAL:(2017)); U.S. Food and Drug Administration (FDA). Software as a Medical Device (SAMD): Clinical Evaluation Guidance for Industry and FDA Staff (2017)). The current reliance on manual processes designed for physical devices leads to inefficiencies, inconsistent decisions, and potential risks to patient safety. This Comment proposes a semi-automated, risk-based intake roadmap for Bangladesh’s Directorate General of Drug Administration (DGDA). Drawing on IMDRF, EU MDCG, and U.S. FDA frameworks, it presents a tangible workflow showing which submissions can be automatically triaged, which require human review, and where human override is maintained (European Commission (MDCG). Guidance on Qualification and Classification of Software in Regulation (EU) 2017/745 (MDCG 2019-11) and World Health Organization (WHO) Global Model Regulatory Framework for medical devices including IVDs (draft; WHO) (n.d.)). Key intake fields, escalation rules, and measurable performance indicators are defined. Anchored to Bangladesh’s current DGDA and national digital health context, the proposal identifies specific legal and infrastructural gaps and outlines steps for phased modernization that may guide other LMICs.

This study recruited senior medical information technicians in Japan to evaluate the performance of ChatGPT. The average correct response rates for multiple-choice and written questions were 82.1% and 76.3%, respectively. Although ChatGPT-generated plausible sentences, it failed to describe the appropriate role of medical information technicians in short essay exams.

Infrared array sensor-based fall detection and activity recognition systems have gained momentum as promising solutions for enhancing healthcare monitoring and safety in various environments. Unlike camera-based systems, which can be privacy-intrusive, IR array sensors offer a non-invasive, reliable approach for fall detection and activity recognition while preserving privacy. This work proposes a novel method to distinguish between normal motion and fall incidents by analyzing thermal patterns captured by infrared array sensors. Data were collected from two subjects who performed a range of activities of daily living, including sitting, standing, walking, and falling. Data for each state were collected over multiple trials and extended periods to ensure robustness and variability in the measurements. The collected thermal data were compared with multiple statistical distributions using Earth Mover’s Distance. Experimental results showed that normal activities exhibited low EMD values with Beta and Normal distributions, suggesting that these distributions closely matched the thermal patterns associated with regular movements. Conversely, fall events exhibited high EMD values, indicating greater variability in thermal signatures. The system was implemented using a Raspberry Pi-based stand-alone device that provides a cost-effective solution without the need for additional computational devices. This study demonstrates the effectiveness of using IR array sensors for non-invasive, real-time fall detection and activity recognition, which offer significant potential for improving healthcare monitoring and ensuring the safety of fall-prone individuals.

This study used the default model of ChatGPT on health care information technologist exams in Japan to evaluate the reliability of this model for medical information knowledge in non-English languages. The average correct response rates for all questions in the health care, information technology, and health information systems fields were 86%, 93% and 81%, respectively. ChatGPT lacked knowledge of standards and laws; therefore, the accuracy rates for related questions were low. Thus, the study concludes that careful attention is required when using ChatGPT, because several of the explanations it provides lack correct descriptions.

Introduction The Chat Generative Pretrained Transformer (ChatGPT) has developed rapidly and is used in many fields, including healthcare informatics. This study evaluated ChatGPT (GPT-4V)'s performance on the Healthcare Information Technologist (HCIT) certification exam in Japan, which assesses certified professionals who work with electronic health records to improve patient care. Methodology Four hundred seventy-six questions from the HCIT exam were targeted over three years. ChatGPT (GPT-4V) was tested on its ability to answer questions from an HCIT exam to determine if it could perform as well as or better than aspirants taking the exam. Moreover, its performance was evaluated for each academic category, format, presence or absence of images, and calculations. Results The mean correct answer rate for all questions was 84%. ChatGPT achieved the exam passing criteria. The correct answer rate for simple-choice (A-type) questions was higher than that for multiple-choice (X2-type) questions (P < 0.05). The success rate for questions with images was lower than for text-only questions (P < 0.01), and the success rate for questions requiring calculations was lower than for those without calculations (P < 0.05). Conclusions ChatGPT (GPT-4V) met the passing criteria for the 19th to 21st HCIT exams, suggesting that its performance is effective in passing the HCIT exam. ChatGPT may possess the minimum required knowledge, understanding, and application skills for the HCIT certification.

This study presents a comparative analysis of hyper-parameter optimization methods used in developing predictive models for patients at risk of heart failure readmission and mortality. We evaluated three optimization approaches—Grid Search (GS), Random Search (RS), and Bayesian Search (BS)—across three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). The models were built using real patient data from the Zigong Fourth People’s Hospital, which included 167 features from 2008 patients. The mean, MICE, kNN, and RF imputation techniques were implemented to handle missing values. Our initial results showed that SVM models outperformed the others, achieving an accuracy of up to 0.6294, sensitivity above 0.61, and an AUC score exceeding 0.66. However, after 10-fold cross-validation, the RF models demonstrated superior robustness, with an average AUC improvement of 0.03815, whereas the SVM models showed potential for overfitting, with a slight decline (−0.0074). The XGBoost models exhibited moderate improvement (+0.01683) post-validation. Bayesian Search had the best computational efficiency, consistently requiring less processing time than the Grid and Random Search methods. This study reveals that while model selection is crucial, an appropriate optimization method and imputation technique significantly impact model performance. These findings provide valuable insights for developing robust predictive models for healthcare applications, particularly for heart failure risk assessment.

Modern medicine has become dependent on devices that require a stable supply of electricity. This has had a great impact on ICT, which has become integral to the efficient operation of hospitals and medical clinics. All patient information is now integrated into an electronic hospital information system, and equipment that supports medical care, such as dialysis machines, sterilizers, water purification systems, air conditioners, and lighting, cannot operate without electric power. Maintaining a stable power supply is critical. Countermeasures against power outages (including momentary voltage dips) include batteries for short term backup and the installation of private power generators for longer outages. As the chairperson and vice-chairperson of the revision taskforce, we were actively involved in revising the Japanese standards for the provision of emergency power supply in medical institutions, which were issued on December 20, 2023. Here, we describe how the standards impact the establishment of a power supply system and discuss issues related to EMC and the power supply equipment of Japanese hospitals.

Voice communication is essential to effective exchange and sharing of instructions and patient information in clinical settings, with most communication between inpatients and staff (usually nurses) done verbally person-to-person or through the nurse call system. In this paper, we describe the current status of mobile voice communication systems, which have become indispensable to modern hospitals. We also discuss potential alternatives to the PHS systems that have been widely used in Japan but are being phased out, and show the features of four post-PHS candidates that can be used as a nurse call system. All systems have their advantages and disadvantages, but systems that function in the dedicated frequency band of 1.9 GHz, which is currently operating stably in hospitals, will have advantages. We recommend sXGP from the viewpoint of technology and minimal impact on other systems.

Heart failure is a leading cause of death among people worldwide. The cost of treatment can be prohibitive, and early prediction of heart failure would reduce treatment costs to patients and hospitals. Improved readmission prediction would also greatly help hospitals, allowing them to manage their treatment programs and budgets better. This literature review aims to summarize recent studies of predictive analytics models that have been constructed to predict heart failure risk, readmission, and mortality. Random forest, logistic regression, neural networks, and XGBoost were among the most common modeling techniques applied. Most selected studies leveraged structured electronic health record data, including demographics, clinical values, lifestyle, and comorbidities, with some incorporating unstructured clinical notes. Preprocessing through imputation and feature selection were frequently employed in building the predictive analytics models. The reviewed studies exhibit demonstrated promise for predictive analytics in improving early heart failure diagnosis, readmission risk stratification, and mortality prediction. This review study highlights rising research activities and the potential of predictive analytics, especially the implementation of machine learning, in advancing heart failure outcomes. Further rigorous, comprehensive syntheses and head-to-head benchmarking of predictive models are needed to derive robust evidence for clinical adoption.

Most hospitals have introduced Information and Communication Technology (ICT), and the COVID-19 pandemic has resulted in the increased use of wireless communication to achieve a more “contactless” medical environment. The most common applications of wireless communication systems in hospitals include patient data collection and sharing, voice communication, and authentication. Transmission of physician's instructions that are input to the hospital information system can now be done through wireless LAN functions in medical settings. Such systems can be expected to greatly expand in the future. However, problems with the reliability of data transmission in wireless communication systems have arisen. In a survey by the Japanese government, nearly half of the hospitals answered that they had experienced problems with wireless communication. Here, we describe architectural problems that may become obstacles to the future expansion of wireless communication, then propose countermeasures.

More than 90% of Japanese hospitals have installed wireless LANs and 80% of them have Wireless Medical Telemetry Systems (WMTS). Wireless communication has become an important part of the infrastructure of modern hospitals, and voice communication systems have become indispensable as communication tools. Unfortunately, there have been communication failures caused by inappropriate implementation of wireless communication systems or by a poor electromagnetic environment, such as from electromagnetic noise. Inappropriate information sharing of the materials used in hospital construction is also a factor that causes problems. In this paper, we describe the use of wireless communication in Japanese hospitals, identify problems and their causes, and describe the required countermeasures by referring two Japanese guidelines.

Wireless medical telemetry systems (WMTSs) are typical radio communication-based medical devices that monitor various biological parameters, such as electrocardiograms and respiration rates. In Japan, the assigned frequency band for WMTSs is 400 MHz. However, the issues accounting for poor reception in WMTS constitute major concerns. In this study, we analyzed the effects of electromagnetic interferences (EMIs) caused by other radio communication systems, the intermodulation (IM) effect, and noises generated from electrical devices on WMTS and discussed their management. The 400-MHz frequency band is also shared by other radio communication systems. We showed the instantaneous and impulsive voltages generated from the location-detection system for wandering patients and their potential to exhibit EMI effects on WMTS. Further, we presented the IM effect significantly reduces reception in WMTS. Additionally, the electromagnetic noises generated from electrical devices, such as light-emitting diode lamps and security cameras, can exceed the 400 MHz frequency band as these devices employ the switched-mode power supply and/or central processing unit and radiate wideband emissions. Moreover, we proposed and evaluated simple and facile methods using a simplified spectrum analysis function installed in the WMTS receiver and software-defined radio for evaluating the electromagnetic environment.

Machine learning shows promise for predicting the risk of heart failure. However, various recent studies have relied only on the electronic health records and clinical data gleaned from datasets from specific regions or healthcare systems, which may limit the generalizability of their predictive models. Our comparative data analysis explores the impact of the diversity of the data of two datasets that contain real-world patient data. The demographic characteristics, comorbidities, clinical and physical values, and laboratory findings of these heart failure datasets were analyzed by the use of data visualization and statistical analysis approaches to investigate possible correlations between the above features and all-cause readmission or death. This study revealed similarities and differences in data specification between the two datasets. Through data visualization, both datasets showed comparable distribution for each feature. The statistical analysis showed variables highly related to and correlated with the outcomes from both datasets.

Wireless LANs in hospitals have mainly been used to link hospital information system servers and terminals, and the use of medical devices with wireless LAN-based communication functions is increasing in Japan. Several standards for wireless LANs are in use, and compatibility with the standard used by a device is important when connecting it to an existing system. Here, we report information on the availability of standards for Japanese medical devices that can communicate via wireless LAN. An internet search found only one radiological system and one syringe pump that identified the wireless LAN standard they followed. There was no mention of the standard used in any of the accompanying documents, even though manufacturers are obliged publish them under the Pharmaceutical Equipment Law. In contrast, for Europe the catalogues and declarations of conformity linked the webpage for the devices contained the necessary information on the standard. However, our search of the wireless LAN standards of other countries found few information on the standards for some infusion pumps. It is our hope that this critical information on standards be made widely available in Japan and in other countries as soon as possible.

This systematic review aimed to assess the academic potential of ChatGPT (GPT-3.5, 4, and 4V) for Japanese national medical and healthcare licensing examinations, taking into account its strengths and limitations. Electronic databases such as PubMed/Medline, Google Scholar, and ICHUSHI (a Japanese medical article database) were systematically searched for relevant articles, particularly those published between January 1, 2022, and April 30, 2024. A formal narrative analysis was conducted by systematically arranging similarities and differences between individual research findings together. After rigorous screening, we reviewed 22 articles. Except for one article, all articles that evaluated GPT-4 showed that this tool could pass each exam containing text only. However, some studies also reported that, despite the possibility to pass, the results of GPT-4 were worse than those of the actual examinee. Moreover, the newest model GPT-4V insufficiently recognized images, thereby providing insufficient answers to questions that involved images and figures/tables. Therefore, their precision needs to be improved to obtain better results.

This research uses a low-resolution infrared array sensor to address real-time human activity recognition while prioritizing the preservation of privacy. The proposed system captures thermal pixels that are represented as a human silhouette. With camera and image processing, it is easy to detect human activity, but that reduces privacy. This work proposes a novel human activity recognition system that uses interpolation and mathematical measures that are unobtrusive and do not involve machine learning. The proposed method directly and efficiently recognizes multiple human states in a real-time environment. This work also demonstrates the accuracy of the outcomes for various scenarios using traditional ML approaches. This low-resolution IR array sensor is effective and would be useful for activity recognition in homes and healthcare centers.

This report investigates three fundamental search algorithms: Linear Search, Binary Search, and Two Pointer Search. Linear Search checks each element sequentially, Binary Search divides the search space in half, and Two Pointer Search uses two pointers to scan from both ends of a sorted list. We compare these algorithms in terms of time complexity, space complexity, and practical performance. Our findings demonstrate that while Linear Search is straightforward, it is inefficient for large datasets. Binary Search is efficient for sorted data but requires an initial sorting step. The Two Pointer Search, combining elements of both methods, offers a practical balance of simplicity and efficiency. We propose a novel implementation of the Two Pointer Search algorithm and validate its performance through comprehensive testing on various hardware configurations. The results indicate that our proposed algorithm significantly improves search efficiency, making it suitable for both sorted and unsorted datasets. Future work will extend this algorithm to more complex data structures and real-world applications.

According to a joint survey conducted by the Ministry of Internal Affairs and Communications and the Ministry of Health, Labour, and Welfare of Japan, over 90% of Japanese hospitals have introduced wireless LAN. However, about half of the hospitals that have wireless LANs have reported having experienced problems, with the most common cause being inappropriate management of signal propagation. Other factors include an excessive number of terminals connected to one AP, an information load that approaches or exceeds the limits of the network’s capacity, and a lack of information sharing during design and construction. There is also a move towards converting in-hospital PHS, which has been used mainly for nurse calls, to smartphones, which also provide voice communication over a wireless LAN. Furthermore, the number of medical devices with wireless LAN communication functions is increasing. In recent years, issues have emerged regarding the coexistence of wireless LANs used by patients. This includes security aspects, as with problematic operation of wireless LANs. The above could become even more significant considerations in future wireless LAN utilization. In this paper, we summarize issues we have identified, clarify their causes, and present possible future problems and current and future measures to be taken for their solution.